Monotonicity Detection and Enforcement in Longitudinal Classification.

Longitudinal datasets contain repeated measurements of the same variables at different points in time, which can be used by researchers to discover useful knowledge based on the changes of the data over time. Monotonic relations often occur in real-world data and need to be preserved in data mining models in order for the models to be acceptable by users. We propose a new methodology for detecting monotonic relations in longitudinal datasets and applying them in longitudinal classification model construction. Two different approaches were used to detect monotonic relations and include them into the classification task. The proposed approaches are evaluated using data from the English Longitudinal Study of Ageing (ELSA) with 10 different age-related diseases used as class variables to be predicted. A gradient boosting algorithm (XGBoost) is used for constructing classification models in two scenarios: enforcing and not enforcing the constraints. The results show that enforcement of monotonicity constraints can consistently improve the predictive accuracy of the constructed models. The produced models are fully monotonic according to the monotonicity constraints, which can have a positive impact on model acceptance in real world applications.